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Found 12 papers, 1 papers with code
Can the capability of Large Language Models be described by human ability? A Meta Study
The evaluation benchmarks are categorized into 6 primary abilities and 11 sub-abilities in human aspect.
MegaTTS 3: Sparse Alignment Enhanced Latent Diffusion Transformer for Zero-Shot Speech Synthesis
While recent zero-shot text-to-speech (TTS) models have significantly improved speech quality and expressiveness, mainstream systems still suffer from issues related to speech-text alignment modeling: 1) models without explicit speech-text alignment modeling exhibit less robustness, especially for hard sentences in practical applications; 2) predefined alignment-based models suffer from naturalness constraints of forced alignments.
A General Error-Theoretical Analysis Framework for Constructing Compression Strategies
The exponential growth in parameter size and computational complexity of deep models poses significant challenges for efficient deployment.
Lossless Model Compression via Joint Low-Rank Factorization Optimization
Low-rank factorization is a popular model compression technique that minimizes the error $\delta$ between approximated and original weight matrices.
Compression for Better: A General and Stable Lossless Compression Framework
A key challenge is effectively leveraging compression errors and defining the boundaries for lossless compression to minimize model loss.
FP=xINT:A Low-Bit Series Expansion Algorithm for Post-Training Quantization
We introduce a deep model series expansion framework to address this issue, enabling rapid and accurate approximation of unquantized models without calibration sets or fine-tuning.
Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification
Through attacks on implemented and deployable agents in multi-agent scenarios, we accentuate the realistic risks associated with these vulnerabilities.
Comprehensive Assessment of Toxicity in ChatGPT
Moderating offensive, hateful, and toxic language has always been an important but challenging topic in the domain of safe use in NLP.
A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots
Given the simplicity and effectiveness of the attack method, our study indicates scientific plots indeed constitute a valid side channel for model information stealing attacks.
Two-Stage Co-Segmentation Network Based on Discriminative Representation for Recovering Human Mesh From Videos
However, most of the existing methods focus on the temporal consistency of videos, while ignoring the spatial representation in complex scenes, thus failing to recover a reasonable and smooth human mesh sequence under extreme illumination and chaotic backgrounds. To alleviate this problem, we propose a two-stage co-segmentation network based on discriminative representation for recovering human body meshes from videos.
D-CryptO: Deep learning-based analysis of colon organoid morphology from brightfield images
With D-CryptO, subtle variations in how colon organoids responded to the different chemotherapeutic drugs were detected, which suggest potentially distinct mechanisms of action.
Spatio-temporal Tendency Reasoning for Human Body Pose and Shape Estimation from Videos
Different from them, our STR aims to learn accurate and natural motion sequences in an unconstrained environment through temporal and spatial tendency and to fully excavate the spatio-temporal features of existing video data.
Ranked #61 on
3D Human Pose Estimation
on MPI-INF-3DHP
